Alexandria, Virginia was settled on the west bank of the Potomac River in 1695. It became a slave-trading port and, later, an early dividing line of the Union and Confederacy. Today it's an affluent Washington, D.C. suburb on the southern perimeter of the capital beltway, sixty-four miles of highway circling the bulls-eye of power and influence in U.S. government.
Nighttime traffic between Maryland and Virginia is fast to reckless, but daytime gridlock reveals an expanse of high rises stretching out to Alexandria's West End.
Just out of view from I-95, Suzanne Yoakum-Stover and her research partner Andy Eick are up early in an apartment suite turned office, boiling water for tea and writing software code. They are partners in a tiny consultancy called Mission Focus that builds systems for the military intelligence community in the Department of Defense. They also run a nonprofit called the Institute for Modern Intelligence. Both companies were born for fighting terrorism and protecting American lives, though IMI has broader plans.
Yoakum-Stover's passion is ultra-large scale data. If that makes you think of the buzz you're hearing about "big data," stop, and think much bigger. Her vision of ultra-large scale is one enormous, diverse "Unified Dataspace" on an order of size equal to the entire Internet and then some, with a whole ecosystem of processing to study it (see sidebar: "What is Ultra Large Scale Infrastructure?").
She's already built working foundations of a Dataspace system in an intelligence project for the U.S. Army. Despite one numbing setback, the rest is in the works, where it is revealing hard new challenges, but no fatal flaws. Should it become reality and work as described, it likely represents a new phenomenon in data diversity and sheer scale.
High-tech anti-terrorism grabs your attention, but to understand the story, you need to set aside technology for just a moment to see the mindset that makes this person unlike any CIO or startup entrepreneur you've met. She aspires to be neither, because she sees ULS as a pursuit, not a product.
Stover wades into technical minutia as quickly as you ask, but there's none of the eccentricity you'd expect of a person this educated and outspoken. Disarmingly, she's quickly Suzi to friends, and lights up when explaining ideas, especially ULS. You can hire her brainpower, but it's the sum of her background and her physics Ph.D. that makes her talk like a scientist advocate about the topic of data.
The analogy she is explaining to me in her office is the transition from classical to modern physics and large-scale institutional research that all started a century ago.
"Military intel these days is still very archaic because our work is isolated and narrow," she begins, "and I have this gut feeling we're on the brink of a transition like we saw in physics that will happen for intelligence."
She points at a bookshelf and explains the difference. "We have these wonderful disciplines for physics, biology and less pure things like economics, which may be the closest thing to where we're going with information," she says. "Have you seen how fat those graduate textbooks on economic theory are? We don't have any books like that in the intelligence domain, and we need a public institution to study it."
That is her dream for IMI, something like a Fermi Lab or CERN, where ULS Dataspace infrastructure would draw engineers, academics and scientists to "test their biggest, baddest" algorithms and visualizations. ULS would provide "one enormous cauldron seething and bubbling with applications, algorithms" and an absolutely unheard of scale of data.
But first, it all has to be built and it all has to work. And you can't jump to the end of the story. You need to unravel her background, a Long Island child of the '60s who took her physics doctorate from Stony Brook University to a post-doc associate professorship at the University of Wyoming. She likes wide-open spaces, and Laramie filled the bill until experimental computation in atomic modeling turned into an unfunded dead end.
Stover migrated to the canyons of Manhattan and an artificial intelligence startup using natural language processing that went bankrupt in the dotcom boom. "Building artificial intelligence is a terrible business model," she says. "It's no business model."
She next tried database matching. "What could be more boring than that?" except it used AI and Bayesian statistics and estimation, a learning experience but eventually an intellectual roadblock. She learned graphics and visualization at another startup that went belly up. Discouraged, she nearly became the best high school physics teacher anyone would ever be fortunate enough to learn from.
But a semester into her master's in education, her AI friends reconnected and pulled her into Object Sciences, a small company with some government contract work. Natural language processing and database matching was a solid foot in the door and gave her new options to contemplate. After SAIC bought Object Sciences, she moved to the nonprofit Potomac Institute for Policy Studies, where her science background could shine through in policy development for promising technologies.
When I met her in 2009, Suzi Stover was a civilian consultant building ULS infrastructure for the Distributed Common Ground System-Army. DCGS-A is the toolkit fielded by the Army to help everyone from soldiers to think tank analysts battle terrorism.
Ideally, intel units would like a system that can dive into all kinds of data at once, one that doesn't take months to create - or recreate for another use, as she explains.